.github/plugins/azure-skills/skills/appinsights-instrumentation/SKILL.md
Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
npx skillsauth add microsoft/azure-skills appinsights-instrumentationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill provides guidance and reference material for instrumenting webapps with Azure Application Insights.
⛔ ADDING COMPONENTS?
If the user wants to add App Insights to their app, invoke azure-prepare instead. This skill provides reference material—azure-prepare orchestrates the actual changes.
The app in the workspace must be one of these kinds
Find out the (programming language, application framework, hosting) tuple of the application the user is trying to add telemetry support in. This determines how the application can be instrumented. Read the source code to make an educated guess. Confirm with the user on anything you don't know. You must always ask the user where the application is hosted (e.g. on a personal computer, in an Azure App Service as code, in an Azure App Service as container, in an Azure Container App, etc.).
If the app is a C# ASP.NET Core app hosted in Azure App Service, use AUTO guide to help user auto-instrument the app.
Manually instrument the app by creating the AppInsights resource and update the app's code.
Use one of the following options that fits the environment.
No matter which option you choose, recommend the user to create the App Insights resource in a meaningful resource group that makes managing resources easier. A good candidate will be the same resource group that contains the resources for the hosted app in Azure.
development
# Azure App Onboard Scaffold — IaC Generation + Self-Review Generate deployment-ready infrastructure code from an architecture plan, verify it with adversarial self-review, and bridge to validation — all without deploying. ## Quick Reference | Property | Value | |----------|-------| | Parent | [azure-app-onboard](../SKILL.md) | | Best for | Turning `prepare-plan.json` service list into Bicep templates with secure-by-default patterns | | Inputs | `prepare-plan.json` (services, naming, quotas),
development
Assess and migrate cross-cloud workloads to Azure with reports and code conversion. Supports Lambda→Functions, Beanstalk/Heroku/App Engine→App Service, Fargate/Kubernetes/Cloud Run/Spring Boot→Container Apps. WHEN: migrate Lambda to Functions, AWS to Azure, migrate Beanstalk, migrate Heroku, migrate App Engine, Cloud Run migration, Fargate to ACA, ECS/Kubernetes/GKE/EKS to Container Apps, Spring Boot to Container Apps, cross-cloud migration.
development
Use when deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.
devops
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).